How Scientists and Engineers Got It Right, and VC’s Got It Wrong

Scientists and engineers as founders and startup CEOs is one of the least celebrated contributions of Silicon Valley.

It might be its most important.
———-

ESL, the first company I worked for in Silicon Valley, was founded by a PhD in Math and six other scientists and engineers. Since it was my first job, I just took for granted that scientists and engineers started and ran companies.  It took me a long time to realize that this was one of Silicon Valley’s best contributions to innovation.

Cold War Spin Outs
In the 1950’s the groundwork for a culture and environment of entrepreneurship were taking shape on the east and west coasts of the United States. Each region had two of the finest research universities in the United States, Stanford and MIT, which were building on the technology breakthroughs of World War II and graduating a generation of engineers into a consumer and cold war economy that seemed limitless. Each region already had the beginnings of a high-tech culture, Boston with Raytheon, Silicon Valley with Hewlett Packard.

However, the majority of engineers graduating from these schools went to work in existing companies.  But in the mid 1950’s the culture around these two universities began to change.

Stanford – 1950’s Innovation
At Stanford, Dean of Engineering/Provost Fred Terman wanted companies outside of the university to take Stanford’s prototype microwave tubes and electronic intelligence systems and build production volumes for the military. While existing companies took some of the business, often it was a graduate student or professor who started a new company. The motivation in the mid 1950’s for these new startups was a crisis – we were in the midst of the cold war, and the United States military and intelligence agencies were rearming as fast as they could.

Why It’s “Silicon” Valley
In 1956 entrepreneurship as we know it would change forever.  At the time it didn’t appear earthshaking or momentous. Shockley Semiconductor Laboratory, the first semiconductor company in the valley, set up shop in Mountain View. Fifteen months later eight of Shockley’s employees (three physicists, an electrical engineer, an industrial engineer, a mechanical engineer, a metallurgist and a physical chemist) founded Fairchild Semiconductor.  (Every chip company in Silicon Valley can trace their lineage from Fairchild.)

The history of Fairchild was one of applied experimentation. It wasn’t pure research, but rather a culture of taking sufficient risks to get to market. It was learning, discovery, iteration and execution.  The goal was commercial products, but as scientists and engineers the company’s founders realized that at times the cost of experimentation was failure. And just as they don’t punish failure in a research lab, they didn’t fire scientists whose experiments didn’t work. Instead the company built a culture where when you hit a wall, you backed up and tried a different path. (In 21st century parlance we say that innovation in the early semiconductor business was all about “pivoting” while aiming for salable products.)

The Fairchild approach would shape Silicon Valley’s entrepreneurial ethos: In startups, failure was treated as experience (until you ran out of money.)

Scientists and Engineers as Founders
In the late 1950’s Silicon Valley’s first three IPO’s were companies that were founded and run by scientists and engineers: Varian (founded by Stanford engineering professors and graduate students,) Hewlett Packard (founded by two Stanford engineering graduate students) and Ampex (founded by a mechanical/electrical engineer.) While this signaled that investments in technology companies could be very lucrative, both Shockley and Fairchild could only be funded through corporate partners – there was no venture capital industry. But by the early 1960′s the tidal wave of semiconductor startup spinouts from Fairchild would find a valley with a growing number of U.S. government backed venture firms and limited partnerships.

A wave of innovation was about to meet a pile of risk capital.

For the next two decades venture capital invested in things that ran on electrons: hardware, software and silicon. Yet the companies were anomalies in the big picture in the U.S. – there were almost no MBA’s. In 1960’s and ‘70’s few MBA’s would give up a lucrative career in management, finance or Wall Street to join a bunch of technical lunatics. So the engineers taught themselves how to become marketers, sales people and CEO’s. And the venture capital community became comfortable in funding them.

Medical Researchers Get Entrepreneurial
In the 60’s and 70’s, while engineers were founding companies, medical researchers and academics were skeptical about the blurring of the lines between academia and commerce. This all changed in 1980 with the Genentech IPO.

In 1973, two scientists, Stanley Cohen at Stanford and Herbert Boyer at UCSF, discovered recombinant DNA, and Boyer went on to found Genentech. In 1980 Genentech became the first IPO of a venture funded biotech company. The fact that serious money could be made in companies investing in life sciences wasn’t lost on other researchers and the venture capital community.

Over the next decade, medical graduate students saw their professors start companies, other professors saw their peers and entrepreneurial colleagues start companies, and VC’s started calling on academics and researchers and speaking their language.

Scientists and Engineers = Innovation and Entrepreneurship
Yet when venture capital got involved they brought all the processes to administer existing companies they learned in business school – how to write a business plan, accounting, organizational behavior, managerial skills, marketing, operations, etc. This set up a conflict with the learning, discovery and experimentation style of the original valley founders.

Yet because of the Golden Rule, the VC’s got to set how startups were built and managed (those who have the gold set the rules.)

Fifty years later we now know the engineers were right. Business plans are fine for large companies where there is an existing market, product and customers, but in a startup all of these elements are unknown and the process of discovering them is filled with rapidly changing assumptions.

Startups are not smaller versions of large companies. Large companies execute known business models. In the real world a startup is about the search for a business model or more accurately, startups are a temporary organization designed to search for a scalable and repeatable business model.

Yet for the last 40 years, while technical founders knew that no business plan survived first contact with customers, they lacked a management tool set for learning, discovery and experimentation.

Earlier this year we developed a class in the Stanford Technology Ventures Program, (the entrepreneurship center at Stanford’s School of Engineering), to provide scientists and engineers just those tools – how to think about all the parts of building a business, not just the product. The Stanford class introduced the first management tools for entrepreneurs built around the business model / customer development / agile development solution stack. (You can read about the class here.)

So what?

Starting this Thursday, scientists and engineers across the United States will once again set the rules.

Stay tuned for the next post.
Listen to the post here: Download the Podcast here

Reinventing the Board Meeting – Part 2 of 2 – Virtual Valley Ventures

There is nothing more powerful than an idea whose time has come
Victor Hugo

When The Boardroom is Bits
A revolution has taken hold as customer development and agile engineering reinvent the Startup process. It’s time to ask why startup board governance has failed to keep pace with innovation. Board meetings that guide startups haven’t changed since the early 1900’s.

It’s time for a change.

Reinventing the board meeting may allow venture-backed startups a more efficient, productive way to direct and measure their search for a profitable business model.

Reinventing the board meeting may offer angel-funded startups that don’t have formal boards or directors (because of geography or size of investment) to attract experienced advice and investment outside of technology clusters (i.e. Silicon Valley, New York).

Here’s how.

A Hypothesis – The Boardroom As Bits
Startups now understand what they should be doing in their early formative days is search for a business model. The process they use to guide their search is customer development. And to track their progress startups now have a scorecard to document their week-by-week changes – the business model canvas.

Yet even with all these tools, early stage startups still need to physically meet with advisors and investors. That’s great if you can get it.  But what if you can’t?

What’s missing is a way to communicate all this complex information and get feedback and guidance for startups who cannot get advice in a formal board meeting.

We propose that early stage startups communicate in a way that didn’t exist in the 20th century – online – collaboratively through blogs.

We suggest that the founders/CEO invest 1 hour a week providing advisors and investors with “Continuous Information Access” by blogging and discussing their progress online in their startup’s search for a business model. They would:

What Does this Change?
1) Structure. Founders operate in a chaotic regime. So it’s helpful to have a structure that helps “search”
 for a business model. The “boardroom as bits” uses Customer Development as the process for the search, and the business model canvas as the scorecard to keep track of the progress, while providing a common language for the discussion.

This approach offers VC’s and Angels a semi-formal framework for measuring progress and offering their guidance in the “search”
 for a business model. It turns ad hoc startups into strategy-driven startups.

2) Asynchronous Updates. Interaction with advisors and board members can now be decoupled from the – once every six weeks, “big event” – board meeting. Now, as soon as the founders post an update, everyone is notified. Comments, help, suggestions and conversation can happen 24/7. For startups with formal boards, it makes it easy to implement, track, and follow-up board meeting outcomes.

Monitoring and guiding a small angel investment no longer requires the calculus to decide whether the investment is worth a board commitment. It potentially encourages investors who would invest only if they had more visibility but where the small number of dollars doesn’t justify the time commitment.

A board as bits ends the repetition of multiple investor coffees. It’s highly time-efficient for investor and founder alike.

3) Coaching. This approach allows real-time monitoring of a startup’s progress and zero-lag for coaching and course-correction.  It’s not just a way to see how they’re doing. It also provides visibility for a deep look at their data over time and facilitates delivery of feedback and advice.

4) Geography. When the boardroom is bits, angel-funded startups can get experienced advice – independent of geography. An angel investor or VC can multiply their reach and/or depth. In the process it reduces some of the constraints of distance as a barrier to investment.

Imagine if a VC took $4 million (an average Series A investment) and instead spread it across 40 deals at $100K each in a city with a great outward-facing technology university outside of Silicon Valley. In the past they had no way to monitor and manage these investments. Now they can. The result – an instant technology cluster – with equity at a fraction of Silicon Valley prices.  It might be possible to create Virtual Valley Ventures.

We Ran the Experiment
At Stanford our Lean Launchpad class ran an experiment that showed when “the boardroom is bits” can make a radical difference in the outcome of an early stage startup.

Our students used Customer Development as the process to search for a business model. The used a blog to record their customer learning, and their progress and issues. The blog became a narrative of the search by posting customer interviews, surveys, videos, and prototypes. They used the Business Model Canvas as a scorekeeping device to chart their progress. The result invited comment from their “board” of the teaching team.

Here are some examples of how rich the interaction can become when a management team embraces the approach.

We were able to give them near real-time feedback as they posted their results. If we had been a board rather than a teaching team we would have added physical reality checks with Skype and/or face-to-face meetings.

Show Me the Money
While this worked in the classroom, would it work in the real world? I thought this idea was crazy enough to bounce off a five experienced Silicon Valley VC’s. I was surprised at the reaction – all of them want to experiment with it. Jon Feiber at MDV is going to try investing in startups emerging from Universities with great engineering schools outside of Silicon Valley that have entrepreneurship programs, but minimal venture capital infrastructure. (The University of Michigan is a possible first test.) Kathryn Gould of Foundation Capital and Ann Miura-Ko of Floodgate also want to try it.

Shawn Carolan of Menlo Ventures not only thought the idea had merit but seed-funded the LeanLaunchLab, a startup building software to automate and structure this process. (More than 700 startups signed up for the LeanLaunchLab software the day it was first demo’d.) Other entrepreneurs think this is an idea whose time has come and are also building software to manage this process including Alexander Osterwalder, Groupiter, and Angelsoft. Citrix thought this was such a good idea that their Startup Accelerator has offered to provide GoToMeeting and GoToMeeting HD Faces free to participating VC’s and startups. Contact them here.

Summary
For startups with traditional boards, I am not suggesting replacing the board meeting – just augmenting it with a more formal, interactive and responsive structure to help guide the search for the business model. There’s immense value in face-to-face interaction. You can’t replace body language.

But for Angel-funded companies I am proposing that a “board meeting in bits” can dramatically change the odds of success. Not only does this approach provide a way for founders to “show your work” to potential and current investors and advisors, but also it helps expand opportunities to attract investors from outside the local area.

Lessons Learned

  • Startups are a search for a business model
  • Startups can share their progress/get feedback in the search
  • Weekly blog of the customer development narrative
  • Weekly summary of the business model canvas
  • Interactive comments and questions
  • Skype and face-to-face when needed
  • This may be a way to augment traditional board meetings
  • This might be a way to rethink our notion of geography as a barrier to investments

Or watch the video here.
Listen to the post here: Download the Podcast here

Why Board Meetings Suck – Part 1 of 2

There are none so blind as those who will not see.
Jonathan Swift

What’s Wrong With Today’s Board Meetings
As customer and agile development reinvent the Startup, it’s time to ask why startup board governance has not kept up with the pace of innovation. Board meetings that guide startups haven’t changed since the early 1900’s.

It’s time.

Reinventing the board meeting may offer venture-backed startups a more efficient, productive way to direct and measure their search for a profitable business model.

Reinventing the board meeting may offer angel-funded startups – which because of geography or size of investment typically don’t have formal boards or directors – to attract experienced advice and investment outside of technology clusters (i.e. Silicon Valley, New York).

Here’s how.

Because We’ve Always Done It This Way
The combination of Venture Capital and technology startups is only about 50 years old. Rather than invent a new form of corporate governance, venture investors adopted the traditional board meeting structure from large corporations. Yet boards of large companies exist to monitor efficient strategy and execution of a known business model. While startups eventually get into execution mode, their initial stages are devoted to a non-linear, chaotic search for a business model: finding product/market fit to identify a product or service people will buy in droves at a sustainable, profitable pace.

In the last few years, our understanding that startups are not smaller versions of large companies, made us recognize that startups need their own tools, different from those used in existing companies: Customer Development – the process to search for a Business Model, the Business Model Canvas – the scorecard to measure progress in the search, and Agile Engineering – the tools to physically construct the product.

Yet while we’ve reinvented how startups build their companies, startup investors are still having board meetings like it’s the 19th century.

Why Have a Board Meeting?
From a VC’s point of view there are two reasons for board meetings.

1) It’s their fiduciary responsibility. Once a startup gets going, it has asymmetric information. Investors get board seats to assure themselves and their limited partners that they are duly informed about their investment.

2) Investors believe that their experience and guidance can maximize their return. Here it’s the board that has asymmetric knowledge. A veteran board can bring 50-100x more experience into a board meeting than a first time founder. (VC’s sit on 6 – 12 boards at a time. Assume an average tenure of 4 years per board. Assume two veteran VC’s per board.
=
50-100x more experience.)

From a founder’s point of view there are three reasons for board meetings.

1) It’s an obligation that came with the check.

2) Founders who have a great board do recognize the uncanny pattern recognition skills that good VC’s bring.

3) An experienced board brings an extensive network of customers, partners, help in recruiting, follow-on financing, etc.

What’s Wrong With a Board Meeting?
The Wrong Metrics. Traditional startup board meetings spend an insane amount of wasted time using Fortune 100 company metrics like income statements, cash flow, balance sheet, waterfall charts. The only numbers in those documents that are important in the first year of a startup’s life are burn rate and cash balance. Most board meetings never get past big company metrics to focus on the crucial startup numbers. That’s simply a failure of a startup board’s fiduciary responsibility.

The Wrong Discussions. The most important advice/guidance that should come from investors in a board meeting is about a startup’s search for a business model: What are the business model hypotheses? What are the most important hypotheses to test now? How are we progressing validating each hypothesis? What do those numbers/metrics look like? What are the iterations and Pivots – and why?

Not Real-time.  Startup board meetings occur every 4-6 weeks. While that’s great when you showed up in your horse and buggy, the strategy-to-tactic-to implementation lag is painful at Internet speeds. And unless there’s rigor in the process, because there is no formal structure for follow up, tracking what happened as a result of meeting recommendations and action items gets lost in the daily demands of everyone’s work. (Of course great VC’s mix in coffees, phone calls, coaching and other non-board meeting interactions but it’s ad hoc and not always done.)

Wastes Founders Time. For the founders, “the get ready for the board meeting” drill is often a performance rather than a snapshot. Powerpoints, spreadsheets and rehearsals consume time for materials that are used once and discarded. There are no standards for what each side (board versus management) does. What is the entrepreneur supposed to be doing? What are the board members supposed to be contributing?

The Wrong Structure. If you read advice on how to run a board meeting you’ll get advice that would have felt comfortable to Andrew Carnegie or John D. Rockefeller.

In the age of the Internet why do we need to get together in one room on a fixed schedule? Why do we need to wait a month to six weeks to see progress? Why don’t we have standards for what metrics VC’s want to see from their early stage startup teams?

Angels In America
For angel-funded startups, life is even tougher. Data from the Startup Genome project shows that startups that have helpful mentors, listen to customers, and learn from startup thought leaders raise 7x more money and have 3.5x better user growth. If you’re in a technology cluster like Silicon Valley you may be able to attract ad hoc advice from experienced investors. But very little of it is formal, and almost none of it approaches the 50-100x experience level of professional investors.

As there’s no formal board, most of these angel/investors meetings are over coffees. And lacking a board meeting there’s no formal mechanism to get investor advice. Angel investments in mobile and web apps today are approaching the “throw it against the wall and see if it sticks” strategy.

And for startups outside of technology clusters, there’s almost no chance of attracting Silicon Valley VC’s or angels. Geography is a barrier to investment.

So given all this, the million dollar question is: Why in the age of the Internet haven’t we adopted the tools we build/sell to solve these problems?

In the next post – Reinventing the Board Meeting.

Lessons Learned

  • Early stage board meetings are often clones of large company board meetings
  • That’s very, very wrong
  • Angel-funded startups have no formal mechanism for experienced advice
  • There’s a better way

Listen to the post here: Download the Podcast here

The Lean LaunchPad at Stanford – The Final Presentations

The Stanford Lean LaunchPad class was an experiment in a new model of teaching startup entrepreneurship. This last post – part nine – highlights the final team presentations. Parts one through eight, the class lectures, are here, Guide for our mentors is here. Syllabus is here.

This is the End
Class lectures were over last week, but most teams kept up the mad rush to talk to even more customers and further refine their products. Now they were standing in front of us to give their final presentations. They had all worked hard. Teams spent an average of 50 to 100 hours a week on their companies, interviewed 50+ customers and surveyed hundreds (in some cases thousands) more.

While the slide presentations of each team are interesting to look at, that’s actually the sideshow. What really matters are the business model canvas diagrams in the body and appendix of each presentation. These diagrams are the visual representation of the how and the what a team learned in the class – how they tested their hypotheses by getting out of the building using the Customer Development process and what they learned about each part of their business model.

By comparing the changes the teams made week-to-week-week in their business model canvas diagrams, you’ll see the dynamics of entrepreneurship, as they iterate and Pivot over time. We believe these are the first visual representations of learning over time.

Team Agora

If you can’t see the Agora slides above, click here.

Team Autonomow

If you can’t see the Autonomow slides above, click here.
(p.s. they’re going to make a company out of this class project, and they’re hiring engineers.)

Team Blink Traffic

If you can’t see the Blink traffic slides above, click here.

Team D.C. Veritas

If you can’t see the D.C. Veritas slides above, click here.

Team Mammoptics

If you can’t see the Mammoptics slides above, click here.

Team OurCrave

If you can’t see the OurCrave slides above, click here.

Team PersonalLibraries

If you can’t see the PersonalLibraries slides above, click here.

Team PowerBlocks

If you can’t see the PowerBlocks slides above, click here.

Team Voci.us

If you can’t see the Voci.us slides above, click here.

———

Why Did We Teach This Class?
Many entrepreneurship courses focus on teaching students “how to write a business plan.” Others emphasize how to build a product. We believe the former is simply wrong and the later insufficient.

Business plans are fine for large companies where there is an existing market, existing product and existing customers, but in a startup all of these elements are unknown and the process of discovering them is filled with rapidly changing assumptions. Experienced entrepreneurs realize that no business plan survives first contact with customers. So our goal was to teach something actually useful in the lives of founders.

Building a product is a critical part of a startup, but just implementing build, measure, learn without a framework to understand customers, channel, pricing, etc. is just another engineering process, not building a business. In the real world a startup is about the search for a business model or more accurately, startups are a temporary organization designed to search for a scalable and repeatable business model. Therefore we developed a class to teach students how to think about all the parts of building a business, not just the product.

There was no single class to teach aspiring entrepreneurs all the skills involved in searching for a business model (business model design, customer and agile development, design thinking, etc.) in one quarter. The Lean LaunchPad was designed to fill that void.

What’s Different About the Class?
The Lean LaunchPad class was built around the business model / customer development / agile development solution stack. Students started by mapping their assumptions (their business model) and then each week they tested these hypotheses with customers and partners outside in the field (customer development) and used an iterative and incremental development methodology (agile development) to build the product.

The students were challenged to get users, orders, customers, etc. (and if a web-based product, a minimum feature set) all delivered in 8 weeks. Our goal was to get students out of the building to test each of the nine parts of their business model, understand which of their assumptions were wrong, make adjustments and continue to iterate based on what they learned.  They learned first-hand that faulty assumptions were not a crisis, but a learning event called a pivot —an opportunity to change the business model.

What Surprised Us?

  1. The combination of the Business Model Canvas and the Customer Development process was an extremely efficient template for the students to follow – even more than we expected.
  2. It drove a hyper-accelerated learning process which led the students to a “information dense” set of conclusions. (Translation: they learned a lot more, in a shorter period of time than in any other entrepreneurship course we’ve ever taught or seen.)
  3. The process worked for all types of startups – not just web software but from a diverse set of industries – wind turbines, autonomous vehicles and medical devices.
  4. Insisting that the students keep a weekly blog of their customer development activities gave us insight into their progress in powerful and unexpected ways. (Much more on this in subsequent blog posts.)

What Would We Change?

  1. In this first offering of the Lean Launchpad class we let students sign up without being part of a team. In hindsight this wasted at least a week of class time. Next year we’ll have the teams form before class starts. We’ll hold a mixer before the semester starts so students can meet each other and form teams. Then we’ll interview teams for admission to the class.
  2. Make Market Size estimates (TAM, SAM, addressable) part of Week 2 hypotheses
  3. Show examples of a multi-sided market (a la Google) in Week 3 or 4 lectures.
  4. Be more explicit about final deliverables; if you’re a physical product you must show us a costed bill of materials and a prototype. If you’re a web product you need to build it and have customers using it.
  5. Teach the channel lecture (currently week 5) before the demand creation lecture (currently week 4.)
  6. Have teams draw the diagram of “customer flow” in week 3 and payment flows in week 6.
  7. Have teams draw the diagram of a finance and operations timeline in week 9.
  8. Find a way to grade team dynamics – so we can really tell who works well together and who doesn’t.
  9. Video final presentations and post to the web. (We couldn’t get someone in time this year)

It Takes a Village
While I authored these blog posts, the class was truly a team project. Jon Feiber of Mohr Davidow Ventures and Ann Miura-Ko of Floodgate co-taught the class with me (with Alexander Osterwalder as a guest lecturer.) Thomas Haymore was our great teaching assistant. We were lucky to get a team of 25 mentors (VC’s and entrepreneurs) who selflessly volunteered their time to help coach the teams. Of course, a huge thanks to the 39 Stanford students who suffered through the 1.0 version of the class. And finally special thanks to the Stanford Technology Ventures Program; Tom Byers, Kathy Eisenhardt, Tina Selig for giving us the opportunity to experiment in course design.

E245, the Lean LaunchPad will be offered again next Winter.  See you there!
Listen to the post here: Download the Podcast here

The LeanLaunch Pad at Stanford – Class 8: Key Resources, Activities and Expense Model

The Stanford Lean LaunchPad class was an experiment in a new model of teaching startup entrepreneurship. This post – part eight – was the last formal lecture. Parts one through seven of the lectures are here, Syllabus is here.

While this is the last lecture, the teams still have one more week to work on their companies, and then they have their final presentations – for 30% of their grade.  All the teams have crossed the Rubicon. 

Week 8 of the class.
Last week the teams tested their Revenue Models hypotheses: what are customers willing to pay for? This week they were testing their hypotheses about Partners. Partners are the external companies whose product or service combines with your Value Proposition to create a complete customer solution or “whole product” to satisfy customers. For example, Apple needed music from their record label partners to make the original iPod and iTunes experience complete. (The concept of Partners, took some explanation as some teams confused partners with the Distribution Channel.)

The Nine Teams Present
PersonalLibraries was now an on-line “social shopping system.” After a week of hectic customer discovery, the team further refined their new business model. Their minimum viable product would be “Trusted Advice on products tailored to your needs by people and groups relevant to you.” Their initial customer segment were upwardly mobile professionals with $2-10K discretionary purchases/year (excluding travel,) and their revenue model was affiliate program fees.

With the clock ticking down to the end of the class the team appeared to give up sleep for the remainder of the quarter. They contacted a dozen admissions consulting firms, ran three Usertesting.com video interviews on a social shopping tool, surveyed 40 Stanford students on their on-line shopping habits, and then did another survey of 700 Stanford MBA students (!) to find out what books they’d recommend for prospective students. They used that data as their first “trusted advice” for the new website they built in a week. http://insidely.com/books/

Within the week they were #6 in Google search results for “Stanford Admission Books.”

Amazingly it looked like the PersonalLibraries team had restarted the company and found a segment where customers wanted their product. They had another week to go until their final presentations. This looks like a race to the wire.

If you can’t see the slides above, click here.

Autonomow, the robotic farm weeder, spent part of the week investigating Partners that could help them build a more complete offering for farmers. The team talked to an agricultural sensor expert at U.C. Davis, a German applied Laser research group, a California organic farmer who wanted to be an Earlyvangelist, four service partners and three weed/pest management consultants.

On the technology front, last week they tested whether their Carrotbot (their research platform they built to gather data for machine vision/machine learning) could tell the difference between a carrot and a weed in a farm field versus the lab. This week the team started investigating whether the spectral reflectance curves of healthy green plants are different from weeds, and if so could an infrared Hyperspectral imaging camera be better suited than their current visible light camera for weed/plant recognition.

But what got our attention was when they told us they were investigating what it takes to kill a weed in the field. Their answer? With a laser. Way cool.

They spent the week sorting through some basic laser technical questions. How much energy does it take to kill a weed? Answer: About 5 Joules of energy. Next question: How much energy will the laser require? Answer: If the robotic weeder is traveling at 1.5 mph, the laser needs to kill the weed in about 10 milliseconds; therefore the laser needs to put out no more than 500 watts of energy. What wavelength of laser? Answer: The most cost effective wavelength is 800-900nm ~ $20/watt. But water (the main ingredient in a weed) best absorbs light at higher frequencies – think microwaves. Final question: Is the improved absorption efficiency worth the extra cost? Testing for all of these is required.

If you can’t see the slides above, click here.

The next team was D.C. Veritas, building a low cost wind turbine for cities. Last week the team did mass interviews of city officials across the United States to understand the project approval process inside a city. This week they broadened the discussion with interviews with the city planner in Mariposa, Texas and the city engineer from Rapid City, South Dakota.

They worked on understanding their partners. D.C. Veritas needs three types of partners: installers (to reduce their overhead,) certification authorities (who would provide credibility) and government and research labs (for testing facilities).

Of real interest was their evolving view of their revenue model. Instead of selling a city the wind turbine hardware, their revenue model moved to a Wind Power Purchase Agreement, a long term contract with a city to buy the electricity generated by the D.C. Veritas turbines.

If you can’t see the slides above, click here.

The Agora Cloud Services team was now making a tool set for managing Amazon Web Services cloud compute usage. They believed their tools could save customers 30% of their Amazon bill. Their value proposition was to provide service matching, capacity planning and usage monitoring & control.  They had another 3 interviews, this time with potential partners and integrators.

If you can’t see the slides above, click here.

The Week 8 Lecture: Q&A and Summing Up
Our lecture covered Key Resources and Cost Structure. The textbooks for this class were Alexander Osterwalder’s Business Model Generation (along with the Four Steps to the Epiphany). So who better to have as a surprise guest lecturer for our last class than Alexander Osterwalder himself.

His lecture covered: What resources do you need to build your business?  How many people? What kind? Any hardware or software you need to buy? Any IP you need to license?  How much money do you need to raise?  When?  Why? Importance of cash flows? When do you get paid vs. when do you pay others?

Our assignment for the teams during their final week: What’s your expense model? What are the key financials metrics for costs in your business model?  Costs vs. ramp vs. product iteration? Access to resources. Where is the best place for your business? Where is your cash flow break-even point? Assemble a resources assumptions spreadsheet.  Include people, hardware, software, prototypes, financing, etc.  When will you need these resources?  Roll up all the costs from partners, resources and activities in a spreadsheet by time.

The last part of their assignment is their final presentation – a “Lessons Learned” summary of their work over the entire quarter – which will count for 30% of their grade. To help them get ready for their final, one of our mentors plans to hold a mandatory “story-telling” workshop, to assist them in assembling their final presentation.

If you can’t see the slides above, click here.

———

Over the last few weeks as our students presented, we had a growing feeling that we were seeing something extraordinary. Our teaching objective was to take engineers (with a smattering of MBA’s) and give them an immersive hands-on experience of how an idea becomes a profitable business. We taught them theory, methodology, and practice using Customer Development and business model design.

Watching them we realized that we had found a way to increase the information density a student team could acquire in eight short weeks. But what was truly awe-inspiring was the breathtaking speed and tempo of the teams’ Pivots.

All teams had all accomplished something remarkable, but it won’t be clear what a singular achievement this was until we see their final presentations.

Stay tuned for the last post – the Final Presentations and Lessons Learned.
Listen to the post here: Download the Podcast here

The LeanLaunch Pad at Stanford – Class 7: Revenue Model

The Stanford Lean LaunchPad class was an experiment in a new model of teaching startup entrepreneurship. With one week and one more updates to go, this post is part seven. Parts one through six are here, Syllabus is here. 

With a week to go the teams are starting to look like opening night before the big play. Teams are iterating and pivoting right and left, one team threw their entire business model out the window and did a complete restart, and another team was having a meltdown over personalities.

Week 7 of the class.
Last week the teams were testing their hypotheses about their Channel (how a company delivers its value proposition (i.e. its product or service) to its customers. This week they were testing their hypotheses about Revenue Models: what are customers really willing to pay for? How? Are you generating transactional or recurring revenues? Is it a multi-sided market, and if so who’s the user versus who’s the payer.

The Nine Teams Present
The first team up was PersonalLibraries the team making a reference management system for discovering, organizing and citing researchers’ readings. Oops.  No more.  The team looked at the potential revenue and concluded that the outlook for this business with this customer segment was dismal. They decided to do something more dramatic than just a Pivot. They did a restart. They moved from “Reference Libraries” to “Product Libraries”— an on-line social shopping system. (If this had been a real startup rather than a class we would have had the team test many more variants on customer segment, revenue models, channels, etc before such an extreme move.)

They quickly came up with a new business model canvas, value proposition and customer segment.

The team hasn’t been getting much sleep as they have a week and a half to make meaningful progress. Lets see what they can pull off.

If you can’t see the slides above, click here.

Autonomow, the robotic farm weeder had a busy week. In talking to their sales channel (farm equipment dealers) and customers (organic farmers) they realize they have an opportunity to come up with a unique revenue stream. Instead of selling or leasing the equipment they are going to charge for leasing according to weed density in the farm fields. The denser the weeds the higher the rental price per day. Customers and dealers agree that it’s a fair deal.  Wow.

.

On the way to the WorldAg Expo their Carrotbot (their research platform they built to gather data for machine vision/machine learning) hit the farm fields near Avenal, California.

The videos of the robot in the field were priceless.

and

.

At the World Ag Expo in Tulare the team encounters its first potential competitor –  “Robocrop.” (No kidding, I couldn’t make this up.) The Robocrop Precision Guidance System for row crop cultivators uses a camera to shift a hitch so cultivators can cut very close to the plant rows and the Robocrop InRow is a robotic weeder.

If you can’t see the slides above, click here.

The next team was D.C. Veritas, the team building a low cost residential wind turbine wind turbine for cities and utilities.Last week the team pivoted and their wind turbine is now embedded into street and highway light poles.

This week the D.C. Veritas team put it into overdrive and did mass interviews of city officials across the United States. In Palo Alto they talked to the financial and utilities mangers. In Williamstown, West Virginia they spoke to the city planner and a member of the budget committee. In Oklahoma City, Oklahoma it was the city engineer and director of public works. In Amarillo, Texas they had interviews with the head of the bidding process, the Street light manager, Director of Public Works and the utilities engineer.

They quickly got a good handle on the canonical project approval process inside a city.

They combined their understanding of the city approval process with the data they gleaned from customer interviews and developed preliminary archetypes. These represented the different customers in the approval cycle inside a city.

If you can’t see the slides above, click here.

Agora Cloud Services

The Agora team, a marketplace for cloud computing, (a relative island of calm in a turbulent sea of other teams) now believed their business was žproviding a tool set for managing Amazon Web Services cloud compute usage. They believed they could build tools that would save customers 30% of their Amazon bill by provide service matching, capacity planning and usage monitoring & control.  The team was a paragon of steady and relentless progress. They had another 4 interviews with potential customers and consultants.

If you can’t see the slides above, click here.

The Week 7 Lecture: Partners

Our lecture this week covered Partners. Which partners and suppliers leverage your model? Who do you need to rely on?

Our assignment for the teams for next week: What partners will you need? Why do you need them and what are risks? Why will they partner with you? What’s the cost of the partnership?  What are the benefits for an exclusive partnership? What are the incentives and impediments for the partners?

If you can’t see the slides above, click here.

———

The pressure was on. The other five teams were also furiously iterating and pivoting. The JointBuy team (the one that sent out 16,000 emails last week) realized that their low-fidelity website they used to test key concepts needed to get real to attract buyers and sellers in volume. The team pulled a week of all nighters and turned the wireframe prototype into a fully functioning site.

In almost every entrepreneurship class with a team project there’s a team that can’t figure out how to work together. These are the same problems one sees in real startups (disagreements over who controls the vision, team members not pulling their weight, disillusionment with the team direction, individuals uncomfortable in rapid decision making with less than perfect data, etc.) We give the students an escalation path if they’re having interpersonal problems (mentors – to Teaching Assistant – to Professors) to see if they can first worth through the issues without our intervention. While these are always painful we try to teach that they are part of the learning process. Better you encounter the problems in a classroom than after you raised a venture round.

At this point in the class almost all the teams are in a full sprint to the finish line. Next week, the last lecture.

Next week – Class 8 – Resources, Activities and Costs.

Then the final presentations.
Listen to this post here: Download the Podcast here

The LeanLaunch Pad at Stanford – Class 6: Channel Hypotheses

The Stanford Lean LaunchPad class was an experiment with a new model of teaching startup entrepreneurship. With two weeks and two more updates to go, this post is part six. Parts one through five are here, Syllabus is here.

While we’ve been pushing hard on the teams, this week the teaching team was about to get its socks blown off. All the teams were showing us what agile looked like, but this week several would remind us what focused and relentless really meant.

Week 6 of the class.
Last week the teams tested their hypotheses about Customer Relationships (how do they get, keep and grow customers.) This week they were testing their hypotheses about the sales “Channel” – how a company delivers its value proposition (i.e. its product or service) to its customers. There are two major channels: physical channels and virtual (web/mobile) channels. Physical channels include Direct Sales, Rep Firms, Systems Integrators, Value-added Resellers, Distributors, Dealers, Mass Merchandisers, and Original Equipment Manufacturers. Virtual channels include Dedicated e-commerce, Two-step e-distribution and Aggregators.

The Nine Teams Present
The first team up was Autonomow, the robotic mower farm weeder. They believed tthey would sell their robotic weeder to farm equipment dealers and distributors so they interviewed 9 more of them this week. They found that sales to this channel would require a demonstration, and that dealers would have to demo the robotic weeders to the customers. They learned that farmers expect personal and timely service/support. Relationships and trust are important.

Their week 6 business model now looked like this: 

All that we expected. But what they showed us next astonished all of us.

Last week we challenged the team that unless they developed hardware which could tell the difference between a weed and a plant, their business model would be just another set of PowerPoint slides. We expected that at best in the final 3 weeks of class they might build prototype hardware on a lab bench. Instead they built the prototype of an entire weeding robot – in one week. They called it the CarrotBot.

CarrotBot was their research platform to gather data for machine vision/machine learning. They wanted to test: can a machine tell the difference between a weed and a plant in the field? What about under different lighting and soil conditions? Could they train a machine to do this automatically?

The CarrotBot had a high-speed machine vision camera and a high-resolution camera for visual data as well as a panning LIDAR system for sub-millimeter depth measurement. Encoders on the drive motors and RTK-GPS measured precision position and velocity. After they validated the weed detection system, the next step was to arm the CarrotBot with a weed kill system (clove oil, high pressure steam/water, or lasers).

The Autonomow team worked 20-hour days, Wednesday – Monday. (On Wednesday night they got the idea to build a robot. On Thursday they ordered the parts, received them Friday, then built the robot over the next three days. (They got help from another student researcher in robotics and machine learning in the Stanford Artificial Intelligence Lab.)

Their goal is to deploy CarrotBot this week in the farm fields in Avenal, California, on the way to the World Ag Expo.

I’m sure the teaching team gave them some advice, but we were so busy trying to hide our jaws hitting the floor I can’t remember what it was..


If you can’t see the slides above, click here.

Next was D.C. Veritas, the team building a low cost residential wind turbine. This week the team got religion and decided that a major pivot was in order. They ditched the residential market as they realized that a more accessible and profitable customer segment(s) were cities, lighting companies and utilities.

In talking to customers, the team found that cities are actively trying to reduce street lighting costs (retrofitting with LEDs, turning off lights, and charging streetlight fees.) If they redesigned their the wind turbine,  it could be embedded into street and highway light poles. Not only could the turbine power the street lights, but it would make excess energy that could be sold back into the grid. Their value proposition had now changed from a wind turbine supplier to homes, to a distributed power supplier to cities and utilities.

Their channel was still direct sales, but now selling to cities allowed them to sell multiple turbines with a larger order size.

D.C. Veritas estimated that their new total available market was 13 million city street lights in the U.S., plus an unknown number of highway lights.

The feedback from the teaching team was that with a new customer segment identified the team was now in a race against time to provide a meaningful business model before the class ended.


If you can’t see the slides above, click here.

PersonalLibraries was focused on creating a reference management system for discovering, organizing and citing researchers’ readings. Last week the teaching team had suggested that they ought to “run away from the academic researcher market as fast as possible.” Yet like passionate entrepreneurs,  the team ignored our advice and pressed on. (To be fair, one of their team members had built the software and worked on it for awhile.)

This team spoke with 10 more customers and potential channel partners. They heard: “the academic market is terribly small, charging $1 a user for a high volume academic site license is unrealistic, the cost of reaching lab managers is prohibitive, despite poor economics there are many niche competitors, and academic software is a “dinosaur” business with lots of competitors in the space because they started there years ago and aren’t able to pivot out.”  Ouch!

With the evidence piling up, the team is now starting to think about pivoting to other customer segments and/or other pricing models. Should they create a freemium version of their current product?  Should they look at the Document Management market?

Time is running out for the PersonalLibraries team. Two more weeks of the class to go.  Take a look at their presentations and you decide – what should they do?


If you can’t see the slides above, click here.

The Agora Cloud Services team, (a marketplace for cloud computing) spent the week testing their channel hypotheses and further refined their business model canvas. They believed they were going to have inside sales reps, third party cloud computing consultants and their own web channel sales.

The team interviewed another 9 customers and industry experts and attended the Amazon Web Services meetup in San Francisco.


If you can’t see the slides above, click here.

The Week 6 Lecture: Revenue Model

This week’s lecture covered the Revenue Model including questions like these: How does your company make money? What are your customers going to pay for? What types of revenue streams are there? How does the web differ from other channels?

Our assignment for the teams for next week: What are the key financials metrics for your business model? If you have more than one product, how will you package it into various offerings?  How will you price the offerings? What is the customer lifetime value?  How are your competitors pricing? Each team has to test their pricing in front of 100 customers on the web or 10-15 customers non-web. And they had to assemble an income statement for the their business model.


If you can’t see the slide above, click here.

———

Most of the teams were doing great. A few were doing spectacularly well. One other team in the class, Jointbuy (an online platform allowing buyers to purchase products in bulk) turned in an equally extraordinary effort. When testing demand creation in their multi-sided business model, they couldn’t get enough sellers to their site. So they sent out mass emails to create demand. They certainly got noticed – as they had hijacked the Stanford email system to send 16,000 emails before they got shut down.

Much like startups in the real world, team performance in entrepreneurship classes seems to follow a Pareto distribution.

Two weeks to go. Let’s see how tenacity, sleepless nights, customer feedback and agile iteration change the final outcome.

Next week – Class 7 – Revenue Model
Listen to the post here: Download the Podcast here

The LeanLaunch Pad at Stanford – Class 5: Customer Relationship Hypotheses

The Stanford Lean LaunchPad class was an experiment in a new model of teaching startup entrepreneurship. This post is part five. Parts one through four are here, Syllabus is here. 

Week 5 of the class.
Last week the teams were testing their hypotheses about their Customers (who are the users, payers, buyers, etc.)  This week they were testing one of the most confusing sections of a company’s business model – Customer Relationships – the activities used to “Get, Keep and Grow” customers in a physical or virtual (web or mobile) channel. (Internet investor Dave McClure coined the acronym “AARRR,” to remember the parts of Customer Relationships on the web.)

Many of the students had heard phrases that fall under Customer Relationships before; “customer acquisition, SEO/SEM, public relations, Social Network, Advertising, Loyalty programs, cross-sell and up-sell” etc., but now they were actually trying to implement it. (If their team was a web or mobile app they actually had to buy Google or Facebook ads and create demand.)

For some of the teams their expectation was if they built the product customers will come. Filing into the classroom I could tell that for some reality had just come crashing down on them. Seeing the lack of customer interest for the first time is always depressing. (The goal of the class was to get them to understand that in a startup, that was the norm not the exception. And to teach them a methodology of what to do about it.) It was making some of the teams question other parts of their business model (did they have the right customer, did they have the right product features to meet customer needs, etc.)

The Nine Teams Present
The first team to present was D.C. Veritas, the team building a low cost, residential wind turbine. During the week they interviewed 7 more companies and consultants, developed case studies for 20 different cities in 5 states, and finalized the bill of materials for the wind turbine. But the big project for the week was testing and analyzing Customer Acquisition Costs.  The team put together their sales funnel and started testing demand.

The results were disappointing. The most optimistic estimates showed that the residential wind turbine market was less than $20m in year 5 and the costs to acquire the customers made this a money-losing business.

After regrouping the team decided that a major pivot was in order. Perhaps residential customers were the wrong target?  Maybe the wind turbine they were building was better suited to a different customer segment?  They had gotten feedback from consultants and industry experts that cities and utilities might be a more receptive audience. What if they redesigned the wind turbine to be embedded into street and highway light poles?  Then they could serve cities, lighting companies and utilities. Using the business model canvas, the changes to their business were obvious.

(BTW, our definition of a Pivot: it’s when you significantly modify one or more of the business model building blocks.)

Three more weeks to go.  Can the D.C. Veritas team discover whether there’s a real opportunity for their wind turbine in cities? The teaching team observed that the next few weeks are going to be interesting. Time to dig in and find out.

Our next team up was Autonomow, the robot lawn mower farm weeder. Last week they had pivoted from customers who needed large areas mowed, to organic farmers who needed lower costs for weeding. In this weeks foray into farm country they spoke to five farm implement dealers and interviewed yet another farmer. However, their primary focus was thinking through how they would “get” their initial customers. In talking to farmers and farm equipment dealers they learned the farm-specific places to create demand; trade shows like the World Ag Expo and magazines such as Vegetable Grower, Ag Source, Farm Equipment and Tractor House. The team then put together a specific budget for initial demand creation.

The teaching team suggested that was the research to date was great, but until they built a robot that could actual tell the difference between a weed and a plant, this would just be a paper exercise. They were engineers, certainly they could do better than that? The Autonomow team started thinking how they could prove that their paper business model was real.

If you can’t see the slides above, click here.

PersonalLibraries
Last week we asked the PersonalLibraries team: are there enough customers to make this a business?  So during the week they ran more hands-on user testing, A/B tests, landing page conversion tests, and bought Google Adwords.

The results were not impressive. The feedback they were getting was that the product was a “nice to have” but not a “hair-on-fire” product.

Our feedback was, that their data seemed to say that their current users don’t want to spend money and will incur infinite support and infinite cost. Our suggestion was, “run away from the academic researcher market as fast as possible.” We offered that the team  might want to expand their user research to think about new features and verticals (document management, law firms, lab managers with discretionary budget, etc.)

If you can’t see the slides above, click here.

Agora Cloud Services
The Agora team ended last week wondering whether they were 1) a true marketplace for cloud computing, where they provide both matching and exchange capabilities for real-time trading. Or were they 2) an information exchange, providing matching services for cloud computing buyers and sellers, providing matching services.  This week they answered the question by “punting.”  They decided they were going to start as a information services, move to brokering, then prediction and finally evolve into a true market.  They interviewed another 8 buyers/sellers/industry experts.

Their results on whether they could acquire with Google Adwords was a bit sobering. Their first effort didn’t get much traffic: 6 clicks out of ~2000 impressions.  Worse yet, each of these clicks cost about a $1.00.  Reason? They had been bidding on keywords that are too generic (e.g. cloud, ec2, Amazon Web Services, etc.)

Their ads of “Cloud Demand Prediction” hadn’t been catching the eyes of people searching for these keywords.  So they picked more specific keywords such as, (cloud comparison, best cloud providers, etc).  And they created ads with specific headlines, such as “Too many cloud providers?”, “Reduce your cloud spend”, etc).  They also increased their daily campaign budget to $20.00.  What they found was that the keywords that did have traffic volume are extremely expensive. Depending on the keyword, the first page bids were between $5.00 to $25.00 per click! Ouch.

The team concluded that AdWords may not be the best channel to create demand.

If you can’t see the slides above, click here.

The Week 5 Lecture: Channel
Channels are how a company delivers its value proposition (i.e. its product or service) to its customers. There are two major channels – virtual (web/mobile) and physical channels – and the difference is dramatic. In one, physical goods move from a loading dock to a customer or a retail outlet. In another the product is offered and sold online. (If the product is itself bits, it may not only be sold online but is often also delivered or used on-line.)

Our lecture talked out how to choose the right sales channel, how the channel makes money, how they’re motivated, and the economics of a sales channel.

If you can’t see the slide above, click here.

———

The lesson for the students this week was failure. What we wanted to teach them wasn’t how to fail fast – any idiot can do that. We wanted to teach them how to recognize failure, learn from it, and pivot.  It’s not about failing fast – it’s about learning faster. That’s the lesson at the heart of the search for a repeatable and scalable business model.

Now deep into the class most of the teams are starting to rethink their initial assumptions. Which teams will continue to Pivot?  Will any completely abandon their current business and pick a new one?

Stay tuned.

Next week – Class 6 – Distribution Channel Hypothesis

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Listen to the post here: Download the Podcast here

The LeanLaunch Pad at Stanford – Class 4: Customer Hypotheses

The Stanford Lean LaunchPad class was an experiment in a new model of teaching startup entrepreneurship. This post is part four. Part one is here, two is here and three is here. Syllabus is here.

Week 4 of the class.
Last week the teams were testing their hypotheses about their Value Proposition (their company’s product or service.) This week they were testing who the customer, user, payer for the product will be (and discovering if they have a multi-sided business model, one with both buyers and sellers.) Many of them had heard the phrase “product/market fit” before, but now they were living it. And for some of the teams the halcyon days of “we’re taking this class so we can just build our great product and get credit for it” had come to a screeching halt. The news from customers was not good.

Let the real learning begin.

The Nine Teams Present
This week, our first team up was PersonalLibraries (the team that had software to help researchers manage, share and reference the thousands of papers in their personal libraries.) Going into the first four weeks their business model hypotheses looked like this:Last week we told them team: 1) see if the market size was really large enough to support a business, and 2) to find that out they were going to have to 
talk to more customers
 outside of Stanford. So during the past week, the team got feedback from >60 researchers from 
cold calls, in-person interviews, and a web survey.  (We were impressed when we found that they did the in-person interviews by hiring usertesting.com for $39 to set up test scenarios, gave the users specific tasks to accomplish with their minimum viable product, videotaped the customer interactions and summarized customer likes and dislikes.) The good news was that customers said that their minimum viable product (easily organizing research papers) was correct. The bad news was that users would play with their product on-line for a while and leave and never return.  Politely it was described as “poor customer retention” but in reality it was because the product was really hard to use.

But it was their market size survey that had the team (and us) even more concerned; last weeks “hot” market of biomed researchers looked like it was only $30m market, and the total available reference manager market was another $80M.The question was, even if they got the product right, were there enough customers to make it a business?

If you can’t see the slides above, click here.

For next week, they decided to improve the product by adding more tutorials, do a 2nd Customer Survey and begin to create demand for their product with AdWords Value Prop Testing and Landing Page A/B Testing.

The feedback from the teaching team was that customer feedback seems to be saying that this product is a “nice to have” versus “got to have.” Is the lack of excitement the MVP? Users?  Is this a hobby or a business?

Agora Cloud Services
The Agora team started the week wondering whether they were 1) a true marketplace for cloud computing, where they provide both matching and exchange capabilities for real-time trading. Or were they 2) an information exchange, providing matching services for cloud computing buyers and sellers, providing matching services.

They began with a set of questions:

  • What are our new hypothesized value propositions?
  • Which segments have we identified and which do we want to narrow in on?
  • Which value added services do public clouds want to attract customers for?
  • Is there a certain segment of buyers that continually makes purchasing decisions (as opposed to only once at the very beginning of a company).
  • How can we attract buyers to our channel before they make purchasing decisions?
  • Longer-term work/planning: what other experiments should we be constructing
  • Sales process: buyer/ user/ influencer etc.?  Demand generation?

The Agora team decided to formalize the customer discovery process by coming up with a set of Customer Discovery principles and questions that were as good as any I’ve seen.

They had 16 interviews with target customers (Zynga, Yahoo, VMware, Walmart,  Zeconder, etc.) as well as channel partners and cloud industry technology consultants.

Agora was in a classic two-sided market (having both buyers and sellers. The Business Model Canvas is a great way to diagram it out. Each side of a market has it’s own Value Proposition, Customer Segment and Revenue Model.) They learned that one their core customer hypothesis about their buyers, “startups would want to buy computing capacity on a “spot market” was wrong. Startups were actually happy with Amazon Web Services. The Agora team was beginning to believe that perhaps their ideal buyers are the companies that have to handle variable and unpredictable workloads.

If you can’t see the slides above, click here.

The Agora team left the week thinking that it was time for a Pivot: find cloud buyers and sellers who need to better predict demand.  Perhaps in market segment: medium-large companies that do 3D modeling and life sciences simulations

The feedback from the teaching team “great Pivot” and very clear Lessons Learned presentation. Keep at it.

(For the teaching team one of the most important ways to track the teams progress was through the weekly blogs we made each team keep. This of this as their on-line diary. They hated doing it, but for us it added a window into their thinking process, allowed us to monitor how much work they were doing, and more importantly let us course correct when needed.

BTW, If I was on the board of a startup with a first time CEO I might even consider asking for this in the first year as they went through Customer Discovery. Yes it takes time, but I bet it’s less than time than you would spend having coffee with an advisor each week.)

D.C. Veritas, was the team building a low cost, residential wind turbine that average homeowners could afford. From a slow start of customer interaction they made major progress in getting out for the building. This week they refined their target market by building a map of potential customers in the U.S. by modeling wind speed, energy costs, homeownership density and green energy incentives. The result was a density map of target customers. They then did face-to-face interviews with 20 customers and got data from 36 more who fit their archetype.  They also interviewed two companies – Solar City and Awea in the adjacent market (residential photovoltaic’s.)

If you can’t see the slide presentation above, click here.

The teaching team offered that unlike solar panels which work anywhere, they’ve narrowed down the geographic areas where their wind turbine was economical. We observed that their total available market was getting smaller daily. After the next week figuring out demand creation costs, they ought to see if the homeowners were still a viable target market for residential wind turbines.

Autonomow, the robot lawn mower, came in with a major Pivot. Instead of a robotic lawn mower, they were now going to focus on robotic weeding and drop mowing as a customer segment. (Once you use the Business Model Canvas to keep score of Customer Discovery a Pivot is easy to define. A Pivot is when you substantively change one or more of the Business Model Canvas boxes.)

Talking to customers convinced the team that the need for robotic weeding was high, there was a larger potential market (organic crop production is doubling every 4 years and accelerating,) and they could make organic produce more affordable (labor cost reduction of 100 to 1) – and could possibly change the organic farming industry!  And as engineers they believed weed versus crop recognition, while hard, was doable.

During the week the team drove the 160 miles round-trip to the Salinas Valley and had on-site interviews with two organic farms. They walked the fields with the farmers, hand-picked weeds with the laborers and got down into the details of the costs of brining in an organic crop.

They also talked by phone to organic farmers in Nebraska and the Santa Cruz mountains.

They acquired quantitative data by going through the 2008 Agricultural Census. Most importantly their model of the customer began to evolve.

If you can’t see the slide above, click here.

Our feedback: could they really build a robot to recognize and weeds and if so how will they kill the weeds without killing the crops?  And are farmers willing to take a risk on untested and radical ideas like robots replacing hand weeding?

The Week 4 Lecture: Customer Relationships
Our lecture this week covered Customer Relationships (a fancy phrase for how will your company create end user demand by getting, keeping and growing customers.) We pointed out that get, keep and grow customers are different for physical versus virtual channels. Then different again for direct and indirect channels. We offered some examples of what a sales funnel looked like. And we described the difference between creating demand for products that solve a problem versus those that fulfill a need.

If you can’t see the slide above, click here.

———

The biggest lesson for the students this week was the entire reason for the class – no business plan survives first contact with customers – as customers don’t behave as per theory. As smart as you are, there’s no way to predict that from inside your classroom, dorm room or cubicle. Some of the teams were coming to grips with it. Others would find reality crashing down harder a bit later.

Next week, Class 5 – each team tests its demand creation hypotheses. The web-based teams needed to have their site up and running and be driving demand to the site with real Search Engine Optimization and Marketing tests.

Listen to the post here: Download the Podcast here

The LeanLaunch Pad at Stanford – Class 3: Value Proposition Hypotheses

The Stanford Lean LaunchPad class was an experiment in a new model of teaching startup entrepreneurship. This post is part three. Part one is here, two is here. Syllabus is here.

Week 3 of the class and our teams in our Stanford Lean LaunchPad class were hard at work using Customer Development to get out of the classroom and test the first key hypotheses of their business model: The Value Proposition. (Value Proposition is a ten-dollar phrase describing a company’s product or service. It’s the “what are you building and selling?”)

The Nine Teams Present
This week, our first team up was PersonalLibraries (the team that made software to help researchers manage, share and reference the thousands of papers in their personal libraries.) To test its Value Proposition, the team had face-to-face interviews with 10 current users and non-users from biomedical, neuroscience, psychology and legal fields.

What was cool was they recorded their interviews and posted them as YouTube videos. They did an online survey of 200 existing users (~5% response rate). In addition, they demoed to the paper management research group at the Stanford Intellectual Property Exchange project (a joint project between the Stanford Law School and Computer Science department to help computers understand copyright and create a marketplace for content). They met with their mentors, and refined their messaging pitch by attending a media training workshop one of our mentors held.

If you can’t see the slides above, click here.

In interviewing biomed researchers, they found one unmet need: the ability to cite materials used in experiments. This is necessary so experiments can be accurately reproduced. This was such a pain point, one scientist left a lecture he was attending to find the team and hand them an example of what the citations looked like.

The team left the week excited and wondering – is there an opportunity here to create new value in a citation tool? What if we could help scientists also bulk order supplies for experiments? Could we help manufacturers, as well, to better predict demand for their products, or perhaps to more effectively connect with purchasers?

The feedback from the teaching team was a reminder to see if the users they were talking to constitute a large enough market and had budgets to pay for the software.

Agora Cloud Services
The Agora team (offering a cloud computing “unit” that Agora will buy from multiple cloud vendors and create a marketplace for trading) had 7 face-to-face interviews with target customers, and spoke to a potential channel partner as well as two cloud industry technology consultants.

They learned that their hypothesis that large companies would want to lower IT costs by selling their excess computing capacity on a “spot market” didn’t work in the financial services market because of security concerns.  However sellers in the Telecom industries were interested if there was some type of revenue split from selling their own excess capacity.

On the buyers’ side, their hypothesis that there were buyers who were interested in reduced cloud compute infrastructure cost turned out not to be a high priority for most companies. Finally, their assumption that increased procurement flexibility for buying cloud compute cycles would be important turned out to be just a “nice to have,” not a real pain. Most companies were buying Amazon Web Services and were looking for value-added services that simplified their cloud activities.

If you can’t see the slides above, click here.

The Agora team left the week thinking that the questions going forward were:

  • žHow do we get past Amazon as the default cloud computing service provider?
  • How viable is the telecom market as a potential seller of computing cycles?
  • We need to further validate buyer & seller value propositions
  • How do we access the buyers and sellers? What sort of sales structure and salesforce does it require?
  • Who is the main buyer(s) and what are their motivations?
  • Is a buying guide/matching service a superior value proposition to marketplace?

The feedback from the teaching team was a reminder that at times you may have a product in search of a solution.

D.C. VeritasD.C. Veritas, the team that was going to build a low cost, residential wind turbine that average homeowners could afford, wanted to provide a renewable source of energy at affordable price.  They started to work out what features a minimum viable product their value proposition would have and began to cost out the first version. The Wind Turbine Minimum Viable Product would have a: Functioning turbine, Internet feedback system, energy monitoring system and have easy customer installation.

The initial Bill of Material (BOM) of the Wind Turbine Hardware Costs looked like: Inverter (1000W): $500 (plug and play), Generator (1000W): $50-100, Turbine: ~$200, Output Measurement: ~$25, Wiring: $20 = Total Material Cost: ~$800-$850

The team also went to the whiteboard and attempted a first pass at who the archetypical customer(s) might be.

To get customer feedback the team posted its first energy survey here and received 27 responses. In their first attempt at face-to-face customer interviews to test their value proposition and problem hypothesis (would people be interested in a residential wind turbine), they interviewed 13 people at the local Farmer’s Market.

If you can’t see the slide presentation above, click here.

The teaching team offered that out of 13 people they interviewed only 3 were potential customers. Therefore the amount of hard customer data they had collected was quite low and they were making decisions on a very sparse data set. We suggested (with a (2×4) that were really going to have to step up the customer interactions with a greater sense of urgency.The teaching team offered that out of 13 people they interviewed only 3 were potential customers. Therefore the amount of hard customer data they had collected was quite low and they were making decisions on a very sparse data set. We suggested (with a 2×4) that were really going to have to step up the customer interactions with a greater sense of urgency.

Autonomow
The last team up was Autonomow, the robot lawn mower. They were in the middle of trying to answer the question of  “what problem are they solving?” They were no longer sure whether they were an autonomous mowing company or an agricultural weeding company.

They spoke to 6 people with large mowing needs (golf course, Stanford grounds keeper, etc.) They traveled to the Salinas Valley and Bakersfield and interviewed 6 farmers about weeding crops. What they found is that weeding is a hugeproblem in organic farming. It was incredibly labor intensive and some fields had to be hand-weeded multiple times per year.

They left the week realizing they had a decision to make – were they a  “Mowing or Weeding” company?

If you can’t see the slide above, click here.

Our feedback: could they really build a robot to recognize and kill weeds in the field?

The Week 3 Lecture: Customers
Our lecture this week covered Customers – what/who are they?  We pointed out the difference between a user, influencer, recommender, decision maker, economic buyer and saboteur. We also described the differences between customers in Business-to-business sales versus business-to-consumer sales.  We talked about multi-sided markets and offered that not only are there multiple customers, but each customer segment has their own value proposition and revenue model.

If you can’t see the slide above, click here.

Getting Out of the Building
Five other teams presented after these four. All of them had figured out the game was outside the building, with some were coming up to speed faster than others. A few of the teams ideas still looked pretty shaky as businesses. But the teaching team held our opinions to ourselves, as we’ve learned that you can’t write off any idea too early. Usually the interesting Pivots happens later. The finish line was a ways off. Time would tell where they would all end up.

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Next week – Class 4 each team tests their Customer Segment hypotheses (who are their customers/users/decision makers, etc.) and report the results of face-to-face customer discovery. That will be really interesting.
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